Such a seismic acquisition method is called simultaneous source or blended acquisition, and is used to acquire seismic surveys with a high trace density at lower cost. Refracted waves and surface-waves propagate along or parallel to the land surface or ocean bottom with a time delay or moveout that is directly proportional to the shot-to-receiver distance along the surface, and are highly affected by near-surface properties. Such seismic waves constitute noise because they carry little or no subsurface information. They have much higher amplitudes than waves that reflect from subsurface formations and should be removed prior to source separation and prior to reflection imaging. A method is disclosed herein for determining surface-consistent properties for the near-surface noise. These properties are used to predict the noise waveforms for the composite simultaneous-source seismograms, and the waveforms are then subtracted or adaptively subtracted from the composite seismograms.
In conventional seismic acquisition, a series of source gathers are recorded in sequence, one source at a time. The source or source array is fired or excited, and response of the receivers are recorded for the duration of the source excitation (sometimes called a sweep for vibroseis sources) plus the listening time, the time it takes for the seismic wave to reach and to reflect from the target. Recording stops, the recorded source gather data or seismogram are written to tape or disk, the sources move to the next source point, and the process is repeated. With the development of new acquisition systems, it has become possible to continuously excite sources without pause to write to tape or even to wait until all reflected energy is returned from the previous excitation. To reduce the acquisition time and thus cost, sources may be fired simultaneously or with small time shifts from the firing of other sources and a composite seismogram recorded. This is called simultaneous sourcing or blended acquisition. The main advantage of the cost reduction is the ability to record surveys with a large range of source to receiver azimuths (multi-azimuth) and improved target illumination. The compromise with such high efficiency methods is interfering noise or crosstalk, where the crosstalk noise results from multiple source excitation or their returns overlap in time. This crosstalk noise will be in the form of desired but misplaced reflected energy but also includes much larger near-surface noise in the form of surface waves and refractions. Sometimes source encoding is performed to reduce the crosstalk noise, but encoding alone is generally not sufficient to eliminate it. Such encoding may include time shifts and for vibrators, differences in phase, frequency, pseudo-random sequences, sweep rate, etc.
Separation of Simultaneous Source Data
Before conventional processing, the individual source gathers must be separated from the composite seismograms. The goal is to produce a separated gather for each source location with little or no crosstalk or interference noise from other sources excited within the same time window. Alternatively, it is possible to image or perform full waveform inversion directly from the composite records. In this case, the migration or inversion method simulates the wave-equation response for the multiple sources with the field encoding pattern, and then applies an imaging condition, but crosstalk noise is still an issue. An imaging condition is a condition applied at each point to a wavefield to extract that part of the wave that has reflected at the given point.
We will define full separation as the generation of multiple seismograms, with each seismogram corresponding to each single individual source location as if the source was excited by itself. In other words the seismic energy from each source in the composite record is moved to the appropriate seismogram and eliminated from other records. Good separation in one step is only possible if the problem or inversion is well-posed, and there are as many input records as output records. In the HFVS method, as described first by Sallas, et al. (see, for example, U.S. Pat. No. 5,721,710), a number of phase-encoded sweeps are recorded for a group of sources, with the number of sweeps equal to or greater than the number of sources. The vibrator signatures (or sweep functions) are then inverted to create a linear separation filter that when applied to the data optimally separates the data generating as many output records as there were sources. Because the number of input records is greater than the number of output records, the sweep inversion is well-posed and can be computed directly from the sweep reference functions. The presence of noise in the composite records does not strongly affect the quality of separation. But the requirement for multiple sweeps requires more time to acquire the data and limits efficiency of acquisition.
For maximum efficiency and minimal cost, it is desirable to use only one shot or sweep per source location. In this case, multiple shot records must be produced from a single composite record. As described by Neelamani, et al. (U.S. Patent Application Publication No. 2010/0097888) this is an ill-posed problem; the number of inputs is less than the number of outputs. The simplest method to generate multiple source gathers is a simple decode and extraction process, sometimes called pseudo-separation or pseudo-deblending. This process replicates the data and parses it to generate the respective source records, one at a time. Each extracted record contains the extracted source record plus all of the interference noise from other shots. Decoding may involve simply shifting the data by the corresponding start time for the respective source and extracting the time window corresponding to the excitation plus listening time. In the case of vibroseis, it may also involve correlating or deconvolving with a corresponding sweep. In this latter case, some cross-correlation noise remains; it is not possible to implement perfectly orthogonal encoding function or sweep, especially considering nonlinear vibrator mechanisms that generate harmonics. A good example of extracted or pseudo-separated seismograms is illustrated by H. Rozemund (“Slip sweep acquisition,” SEG, Expanded Abstracts 15, 64-67 (1996)).
The pseudo-separated seismograms with inference noise may be filtered in some domain to partially mitigate the interference noise. Even without filtering, the imaging or migration process maybe sufficient for simple structural objectives, but if amplitudes are to be used for stratigraphy or for reservoir property estimation, signal-to-noise enhancement or filtering is needed. Filters only remove the crosstalk noise on each seismogram and do not move misplaced energy to the appropriate seismogram or optimize the separation. The most common filtering method is to sort the data to the common receiver domain or other non-shot domain. In the receiver domain, the interference noise appears random and a coherency filter, such as fk-filter, radon filter, median filter, etc., may be used to remove the incoherent noise and keep the more coherent signal. A problem with these filtering methods is that signal is easily damaged and removed with the noise, especially if the noise is large and requires aggressive filtering. In addition, the use of multi-channel filters requires that the survey be recorded with dense sampling in all directions without aliasing.
An improved method involves an initial extraction or pseudo-separation followed by an interactive inversion method as described by Neelamani, et al. (U.S. Patent Application Publication No. 2010/0097888). Because the problem is ill-posed, some additional information is needed, such as an estimate of the source signature and expectations for separation seismograms, such as dips, decreasing amplitudes, coherency, etc. In this method, with each iteration, more of the misplaced seismic energy, i.e. the interference noise, is increasingly moved to the proper record and the separation is improved. A version of the method is described by Dougleris, et al., “Separation of blended data by Iterative estimation and subtraction of blending interference noise,” Geophysics 76, Q9-Q17 (2011)). Note that in this paper, the maximum signal-to-noise enhancement is about 18 dB and the data involve interference only in the form of misplaced reflected signal and no near-surface noise. All high amplitude ground-roll and first arrival events were first removed before summing the gathers to simulate simultaneous source acquisition for this research test. Subsequent patents on use of sparse-type inversion methods for separation include Abma (US Patent Application Publication No. 2010/0299070) and Moore et al. (PCT International Patent Application Publications WO 2010/093896 and WO 2010/123639).
Near-Surface Noise
Large and variable surface-wave noise is a problem for traditional sequential acquisition methods; it is an even bigger problem for simultaneous source acquisition, regardless of whether or not the composite records are imaged directly or are first separated into source gathers. Most separation methods, particularly those that involve multi-channel filters, are compromised if the data contain large interference noise. Much more energy from a seismic source goes into refractions and surface-waves, which travel parallel to the surface of the earth, than travel vertically through the earth, reflect, and return to the surface. Furthermore because of 3D spherical divergence, reflected amplitudes decrease rapidly, varying inversely with the two-way travelpath distance to the reflectors and back to the surface. Because refractions and surface-waves are confined to the surface, their amplitude decreases by a factor proposal to the square root of distance—a much smaller decrease. Such unwanted noise in the form of surface waves may be 40-60 dB larger than the desired reflections and be a problem for iterative ill-posed separation methods. For example, in Mandad's method, a coherency filter is applied and then amplitude thresholding is used to keep the strongest amplitudes during each iteration; this method will keep the strong unwanted ground roll and eliminate the weaker desired reflections. He found only 18 dB of separation for an example without any surface waves.
A patent to Olson, et al. (U.S. Pat. No. 7,869,304) removes noise on the receiver data before applying the HFVS-type separation filter derived by inverting the sweep matrix. The filters include single or 2D multi-channel filters, such as wavelet transform filter, F-X filter, F-K filter, or median despike filter. If the choice is made of a linear noise filter, such as F-K or FX or wavelet transform, then it can applied before or after the linear separation filters under the cumulative property of multiplication; the output is the same. This method does not address the problem of noise for separating high-efficiency and multi-azimuth vibroseis data in which there are fewer sweeps than sources and in which the sweep matrix cannot be inverted. Furthermore, it does not address the 3D multi-azimuth problem discussed below.
Multi-trace filters are often used for surface-wave mitigation. These methods exploit velocity differences; surface waves are typically much slower in velocity than reflections. Velocity filtering methods include FK filtering, radon filters, adaptive filtering, or beam forming (see, for example, U.S. Pat. No. 6,651,007 to Ozbek). A problem with such methods is that they require a sufficiently short distance between each of the receivers so that the recorded ground roll is not aliased, i.e. so that the surface wave is adequately sampled. If the distance is too large, then ground-roll velocity is ambiguous and it has similar apparent velocity to reflections. Another problem with multi-channel filters is that the character of the near-surface noises changes rapidly as a function of location because it is heavily influenced by highly variable near surface properties. Ozbek teaches an adaptive filter method to compensate for these changes. The adaptive method cannot simultaneously adapt to contributions from different sources from different distances and azimuths recorded by the same receiver.
FIGS. 1-3 illustrate the problem of using multi-trace filtering for 3-D data that is recorded by simultaneous sources according to the prior art. We start from conventional data and simulate the case in which the data were generated by simultaneous sourcing by summing records in the computer instead in the field. FIG. 1 shows a map of a standard 3-D orthogonal acquisition geometry with the receivers represented by “x” and the sources represented by “o”. We start with one receiver line 101 and plot the conventional records generated by sources at locations 102-104 in FIGS. 2A-2C. The gathers in FIGS. 2A and 2B were shifted by 0.5 and 0.8 s, respectively, and then three source gathers were then summed to simulate the composite record FIG. 2D, as if the sources 102-104 were excited simultaneously with the small time shifts. An F-K spectra (FIG. 3A) computed from the conventional gather (FIG. 2A) recorded with the single source 102 located on line 101 shows low-velocity surface waves 201 and 301, aliased surface waves 302, and reflections 303. An FK-fan filter could be constructed to remove most of the surface-wave energy outside of the dashed lines 304 as is done for this example of conventional data acquisition. In comparison, we show an F-K plot of the composite simultaneous-source gather (FIG. 2D) in FIG. 3B. Substantial surface wave energy 305 occurs inside the dashed lines 304, and could not be removed without damaging reflections. Because sources 103 and 104 are offline from receiver line 101, part of the surface wave appears flat as shown in 202 and has low wavenumbers. With 3D multi-azimuth simultaneous-source acquisition, it is not possible to construct a narrow-azimuth line of data that goes through all simultaneous source locations, for example the sources 102-104. Thus, some of the energy will be traveling perpendicular to the receiver line with little apparent velocity discrimination compared with reflected energy.
Accordingly there is a need to remove near-surface noise in the form of refractions and surface waves traveling along the surface of the earth or ocean bottom directly from composite records obtained with simultaneous sourcing acquisition and without relying on multi-channel filters. The signal-enhanced seismograms can then be separated into individual source records or imaged directly.